Semantic consistency for graph representation learning. - Jincheng Huang 005; Ping Li 024; Kai Zhang 001 (IJCNN, 2022)

Learning heterophilious edge to drop: A general framework for boosting graph neural networks. - Jincheng Huang 005; Ping Li 024; Rui Huang; Chen Na (CoRR, 2022)

Robust Mid-Pass Filtering Graph Convolutional Networks. - Jincheng Huang 005; Lun Du; Xu Chen 022; Qiang Fu 015; Shi Han; Dongmei Zhang 001 (WWW, 2023)

On Which Nodes Does GCN Fail? Enhancing GCN From the Node Perspective. - Jincheng Huang 005; Jialie Shen 001; Xiaoshuang Shi; Xiaofeng Zhu 001 (ICML, 2024)

Exploring the Role of Node Diversity in Directed Graph Representation Learning. - Jincheng Huang 005; Yujie Mo; Ping Hu 001; Xiaoshuang Shi; Shangbo Yuan; Zeyu Zhang; Xiaofeng Zhu 001 (IJCAI, 2024)

ELU-GCN: Effectively Label-Utilizing Graph Convolutional Network. - Jincheng Huang 005; Yujie Mo; Xiaoshuang Shi; Lei Feng 006; Xiaofeng Zhu 001 (CoRR, 2024)

A graph transformer defence against graph perturbation by a flexible-pass filter. - Yonghua Zhu; Jincheng Huang 005; Yang Chen 028; Robert Amor; Michael Witbrock (Inf. Fusion, 2024)

Revisiting the Role of Heterophily in Graph Representation Learning: An Edge Classification Perspective. - Jincheng Huang 005; Ping Li 024; Rui Huang; Na Chen; Acong Zhang (ACM Trans. Knowl. Discov. Data, 2024)

Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks. - Acong Zhang; Jincheng Huang 005; Ping Li 024; Kai Zhang 001 (ACM Trans. Knowl. Discov. Data, 2024)

The Final Layer Holds the Key: A Unified and Efficient GNN Calibration Framework. - Jincheng Huang 005; Jie Xu 044; Xiaoshuang Shi; Ping Hu 001; Lei Feng 006; Xiaofeng Zhu 001 (CoRR, 2025)

Denoising Structure against Adversarial Attacks on Graph Representation Learning. - Na Chen; Ping Li 024; Jincheng Huang 005; Kai Zhang 001 (ACM Trans. Intell. Syst. Technol., 2025)